code: def opt_xgb_0():
      optimizer = RandomForestOptPro(iterations=2, random_state=1337)
      optimizer.forge_experiment(
          model_initializer=XGBClassifier,
          model_init_params=dict(
              objective="reg:linear",
              max_depth=Integer(2, 20),
              learning_rate=Real(0.0001, 0.5),
              subsample=0.5,
              booster=Categorical(["gbtree", "dart"]),
          ),
      )
      optimizer.go()
      yield optimizer
  
file_path: hyperparameter_hunter/tests/smoke_tests/test_xgboost.py
imports: from hyperparameter_hunter import Environment, CVExperiment, Real, Integer, Categorical
  from hyperparameter_hunter import RandomForestOptPro
  from hyperparameter_hunter.i_o.result_reader import has_experiment_result_file
  from hyperparameter_hunter.utils.learning_utils import get_toy_classification_data
  import pytest
  from xgboost import XGBClassifier
label_desc: Use image node_id 3 for calling a global function with example usage: opt_xgb_0() without return types